We evaluate MoPAC in the simulated control tasks of MuJoCo [1]} included in the OpenAI-gym library [2]}: HalfCheetah-v2, Ant-v2, Hopper-v2, and Walker2d-v2. We compare the average return of MoPAC over 5 trials, consisting of \(1,000\)  true environment interactions per epoch, against the baselines SAC [3]}, MBPO [4]}, and MBRL [5]}. We decided not to compare with [6]} as the absence of code and a detailed algorithm, makes the reproduction of their results prohibitive.
Here, SAC and MBPO are trained according to the hyperparameter settings provided by their respective works, while for MBRL we use the same settings as MoPAC, namely using horizons \(5-15\)  with linear annealing for all tasks. The number of environment interactions per episode is constant across all algorithms. A batch size of \(10,000\)  is chosen for the model rollouts in both MoPAC and MBPO.
